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README.md

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----
-title: El Capitan
-emoji: 🚀
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 5.25.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# El_Kapitán_100b – HF Space
+
+### What this is:
+This is a lightweight RAG chatbot for cross-country skiing, using Gemini API + FAISS + personalization.
+
+### How to use:
+1. Upload your `chunks.json`, `faiss.index`, and `profiles.json` into this Space.
+2. Set your secret key:
+   - Go to Settings → Secrets → Add:
+     - `GEMINI_API_KEY` = your Gemini API key from Google AI Studio
+3. Deploy and test!
+
+POST requests should be sent to `/ask` with JSON:
+```json
+{
+  "question": "How to train in summer?",
+  "userId": "vilda",
+  "profileName": "vilda"
+}
+```
+
+Enjoy smart, personalized coaching responses! 🎿

app.py

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+
+import json
+import os
+import numpy as np
+import faiss
+from sentence_transformers import SentenceTransformer
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import google.generativeai as genai
+
+app = Flask(__name__)
+CORS(app)
+
+# Load Gemini
+genai.configure(api_key=os.environ["GEMINI_API_KEY"])
+gemini_model = genai.GenerativeModel("gemini-1.5-flash")
+
+# Lazy load
+model = None
+chunks = None
+index = None
+
+# Load profiles once
+with open("profiles.json", "r") as f:
+    profiles = json.load(f)
+
+@app.route("/ask", methods=["POST"])
+def ask():
+    global model, chunks, index
+
+    data = request.get_json()
+    question = data.get("question")
+    user_id = data.get("userId")
+    profile_name = data.get("profileName", "").lower()
+
+    if not question or not user_id:
+        return jsonify({"answer": "Missing question or userId"}), 400
+
+    # Lazy load embeddings and FAISS
+    if model is None:
+        model = SentenceTransformer("intfloat/e5-small")
+
+    if chunks is None or index is None:
+        with open("chunks.json", "r") as f:
+            chunks = json.load(f)
+        index = faiss.read_index("faiss.index")
+
+    # Embed question
+    q_emb = model.encode([question])
+    D, I = index.search(np.array(q_emb), k=3)
+    context = "\n\n".join([chunks[i] for i in I[0]])
+
+    # Load profile
+    bullet_points = profiles.get(profile_name, [])
+    profile_context = "\n".join(bullet_points)
+
+    # Prompt
+    prompt = f"""
+You are El_Kapitán_100b, a professional cross-country skiing coach.
+
+User profile:
+{profile_context}
+
+Context:
+{context}
+
+Question:
+{question}
+"""
+
+    try:
+        response = gemini_model.generate_content(prompt)
+        return jsonify({"answer": response.text})
+    except Exception as e:
+        return jsonify({"answer": f"Server error: {str(e)}"})

requirements.txt

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+flask
+flask-cors
+sentence-transformers
+faiss-cpu
+google-generativeai